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the platform, against the entire data set, without replicating or sampling
data. It must enable data scientists to iterate through different models more
quickly to facilitate discovery and experimentation with a “best fit” yield.
3. Manage and Analyze Unstructured Data
For a long time, data has been classified on the basis of its type—structured,
semistructured, or structured. Existing infrastructures typically have barriers
that prevented the seamless correlation and holistic analysis of this data; for
example, independent systems to store and manage these different data
types. We've also seen the emergence of hybrid systems that often let us
down because they don't natively manage all data types.
One thing that always strikes us as odd is that nobody ever affirms the
obvious: organizational processes don't distinguish between data types. When
you want to analyze customer support effectiveness, structured information
about a CSR conversation (such as call duration, call outcome, customer satis-
faction, survey response, and so on) is as important as unstructured informa-
tion gleaned from that conversation (such as sentiment, customer feedback, and
verbally expressed concerns). Effective analysis needs to factor in all compo-
nents of an interaction, and analyze them within the same context, regardless of
whether the underlying data is structured or not. A game-changing analytics
platform must be able to manage, store, and retrieve both unstructured and
structured data. It also has to provide tools for unstructured data exploration
and analysis.
4. Analyze Data in Real Time
Performing analytics on activity as it unfolds presents a huge untapped
opportunity for the analytic enterprise. Historically, analytic models and
computations ran on data that was stored in databases. This worked well
for transpired events from a few minutes, hours, or even days back. These
databases relied on disk drives to store and retrieve data. Even the best-
performing disk drives had unacceptable latencies for reacting to certain
events in real time. Enterprises that want to boost their Big Data IQ need
the capability to analyze data as it's being generated, and then to take
appropriate action. It's about deriving insight before the data gets stored
on physical disks. We refer to this this type of data as streaming data, and
the resulting analysis as analytics of data in motion . Depending on time of
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